dill package documentation¶
dill: serialize all of python¶
dill extends python’s
pickle module for serializing and de-serializing
python objects to the majority of the built-in python types. Serialization
is the process of converting an object to a byte stream, and the inverse
of which is converting a byte stream back to a python object hierarchy.
dill provides the user the same interface as the
pickle module, and
also includes some additional features. In addition to pickling python
dill provides the ability to save the state of an interpreter
session in a single command. Hence, it would be feasible to save an
interpreter session, close the interpreter, ship the pickled file to
another computer, open a new interpreter, unpickle the session and
thus continue from the ‘saved’ state of the original interpreter
dill can be used to store python objects to a file, but the primary
usage is to send python objects across the network as a byte stream.
dill is quite flexible, and allows arbitrary user defined classes
and functions to be serialized. Thus
dill is not intended to be
secure against erroneously or maliciously constructed data. It is
left to the user to decide whether the data they unpickle is from
a trustworthy source.
dill is part of
pathos, a python framework for heterogeneous computing.
dill is in active development, so any user feedback, bug reports, comments,
or suggestions are highly appreciated. A list of issues is located at
https://github.com/uqfoundation/dill/issues, with a legacy list maintained at
dill can pickle the following standard types:
none, type, bool, int, float, complex, bytes, str,
tuple, list, dict, file, buffer, builtin,
python classes, namedtuples, dataclasses, metaclasses,
instances of classes,
set, frozenset, array, functions, exceptions
dill can also pickle more ‘exotic’ standard types:
functions with yields, nested functions, lambdas,
cell, method, unboundmethod, module, code, methodwrapper,
methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor,
dictproxy, slice, notimplemented, ellipsis, quit
dill cannot yet pickle these standard types:
frame, generator, traceback
dill also provides the capability to:
save and load python interpreter sessions
save and extract the source code from functions and classes
interactively diagnose pickling errors
The latest released version of
dill is available from:
dill is distributed under a 3-clause BSD license.
You can get the latest development version with all the shiny new features at:
If you have a new contribution, please submit a pull request.
dill can be installed with
$ pip install dill
To optionally include the
objgraph diagnostic tool in the install:
$ pip install dill[graph]
For windows users, to optionally install session history tools:
$ pip install dill[readline]
pyreadline, >=1.7.1 (on windows)
dill is a drop-in replacement for
pickle. Existing code can be
updated to allow complete pickling using:
>>> import dill as pickle
>>> from dill import dumps, loads
dumps converts the object to a unique byte string, and
the inverse operation:
>>> squared = lambda x: x**2 >>> loads(dumps(squared))(3) 9
There are a number of options to control serialization which are provided
as keyword arguments to several
with protocol, the pickle protocol level can be set. This uses the same value as the
dillto behave a lot more like pickle with certain objects (like modules) pickled by reference as opposed to attempting to pickle the object itself.
with recurse=True, objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary.
with fmode, the contents of the file can be pickled along with the file handle, which is useful if the object is being sent over the wire to a remote system which does not have the original file on disk. Options are HANDLE_FMODE for just the handle, CONTENTS_FMODE for the file content and FILE_FMODE for content and handle.
with ignore=False, objects reconstructed with types defined in the top-level script environment use the existing type in the environment rather than a possibly different reconstructed type.
The default serialization can also be set globally in dill.settings.
Thus, we can modify how
dill handles references to the global dictionary
locally or globally:
>>> import dill.settings >>> dumps(absolute) == dumps(absolute, recurse=True) False >>> dill.settings['recurse'] = True >>> dumps(absolute) == dumps(absolute, recurse=True) True
dill also includes source code inspection, as an alternate to pickling:
>>> import dill.source >>> print(dill.source.getsource(squared)) squared = lambda x:x**2
To aid in debugging pickling issues, use dill.detect which provides tools like pickle tracing:
>>> import dill.detect >>> with dill.detect.trace(): >>> dumps(squared) ┬ F1: <function <lambda> at 0x7fe074f8c280> ├┬ F2: <function _create_function at 0x7fe074c49c10> │└ # F2 [34 B] ├┬ Co: <code object <lambda> at 0x7fe07501eb30, file "<stdin>", line 1> │├┬ F2: <function _create_code at 0x7fe074c49ca0> ││└ # F2 [19 B] │└ # Co [87 B] ├┬ D1: <dict object at 0x7fe0750d4680> │└ # D1 [22 B] ├┬ D2: <dict object at 0x7fe074c5a1c0> │└ # D2 [2 B] ├┬ D2: <dict object at 0x7fe074f903c0> │├┬ D2: <dict object at 0x7fe074f8ebc0> ││└ # D2 [2 B] │└ # D2 [23 B] └ # F1 [180 B]
With trace, we see how
dill stored the lambda (
F1) by first storing
_create_function, the underlying code object (
(which is used to handle code objects), then we handle the reference to
the global dict (
D2) plus other dictionaries (
save the lambda object’s state. A
# marks when the object is actually stored.
Probably the best way to get started is to look at the documentation at
http://dill.rtfd.io. Also see
dill.tests for a set of scripts that
dill can serialize different python objects. You can
run the test suite with
python -m dill.tests. The contents of any
pickle file can be examined with
dill conforms to
pickle interface, the examples and documentation found at
http://docs.python.org/library/pickle.html also apply to
if one will
import dill as pickle. The source code is also generally
well documented, so further questions may be resolved by inspecting the
code itself. Please feel free to submit a ticket on github, or ask a
question on stackoverflow (@Mike McKerns).
If you would like to share how you use
dill in your work, please send
an email (to mmckerns at uqfoundation dot org).
If you use
dill to do research that leads to publication, we ask that you
acknowledge use of
dill by citing the following in your publication:
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, "Building a framework for predictive science", Proceedings of the 10th Python in Science Conference, 2011; http://arxiv.org/pdf/1202.1056 Michael McKerns and Michael Aivazis, "pathos: a framework for heterogeneous computing", 2010- ; https://uqfoundation.github.io/project/pathos
add (or remove) dill types to/from the pickle registry
dillpopulates its types to
pickle.Pickler.dispatch. Thus, all
dilltypes are available upon calling
'import pickle'. To drop all
dilltypes from the
use_dill (bool, default=True) – if True, extend the dispatch table.
- load_types(pickleable=True, unpickleable=True)¶
load pickleable and/or unpickleable types to
dill.typesis meant to mimic the
typesmodule, providing a registry of object types. By default, the module is empty (for import speed purposes). Use the
load_typesfunction to load selected object types to the
pickleable (bool, default=True) – if True, load pickleable types.
unpickleable (bool, default=True) – if True, load unpickleable types.